From Engineering PhD to Data Scientist: An Alumni Spotlight on Yash Shah

At The Data Incubator we run a free eight-week Data Science Fellowship Program to help our Fellows land industry jobs. We love Fellows with diverse academic backgrounds that go beyond what companies traditionally think of when hiring Data Scientists. SYash was a Fellow in our first cohort who landed a job with one of our hiring partners, AppNexus.


Tell us about your background.

I did my undergraduate studies in Biomedical Engineering from DJ Sanghvi College of Engineering in Mumbai University. I then attended the University of Michigan where I got two master’s degrees in Biomedical Engineering and Electrical Engineering. As a part of my PhD research, I used machine learning techniques to find and study multivariate patterns in the functional MRI brain data.

Recently, I joined as a data scientist at AppNexus, a technology company that provides trading solutions and powers marketplaces for internet advertising. Here I am responsible for analyzing the vast amounts of rich data and building models to improve real-time ad serving.


What do you think you got out of The Data Incubator?

Simply, it was a comprehensive learning experience. The Incubator helped polish our existing soft skills and fill in the holes in our technical expertise. However, the main thing The Data Incubator afforded me was immense exposure. Through the program, I got the opportunity to meet with different employers from completely different fields ranging from pharmaceutical, to ad-tech and finance industries among many others. This gave me an amazing window into the world outside of academia. Meeting like-minded peers and networking with ever-encouraging employers was a huge take-away.


What advice would you give to someone who is applying for The Data Incubator, particularly someone with a Biomedical Engineering background?

At The Data Incubator there are so many hiring companies each seeking a varied skill set, there is ample opportunity to find your “perfect match”.

Frankly, I was a little daunted by the uphill task of getting screened and selected for the program. Especially, since I did not come from a background in statistics or computer science. It was only when I met with the other Fellows who had formal training in completely diverse fields, did I realize that each one of us had a different expertise.

Since there are so many hiring companies each seeking a varied skill set, there is ample opportunity to find your “perfect match”. Being shortlisted from an immensely bright pool of students can surely be intimidating, but I would undoubtedly encourage everyone to apply for the workshop regardless. The application rounds are themselves challenging noteworthy experiences.


What advice would you give to foreign PhD students applying for jobs in the US?

Numerous companies from various sectors of the industry are constantly looking for talented individuals. If the hiring company envisions a good match, then they would be willing to make the student candidate an employment offer regardless of their visa status. Most companies are capable of providing visa sponsorships to foreign PhD students and are also accommodating in terms of start dates, benefits, perks, etc. My advice to all foreign PhD students is that there is ample opportunity in academia as well as in the industry, so just make the most of any opportunity that comes your way and the pieces will all fit together on their own. Good luck and feel free to reach out.


What elements of your Biomedical Engineering training are useful for your current work as a data scientist?

I realized that a lot of the skills that I had picked up during my coursework were easily transferrable to other domains. Since I had already dealt with noisy real-world data in the form of analyzing medical images, I found it easy to apply the same logical thinking and analytical approach while solving problems with real-world data in the ad-tech domain. Similar to experimental hypotheses in biomedical engineering, in data science problems, it is of utmost importance to pose the correct questions before diving into data analysis.

For academic research, I mostly used MATLAB and had very little experience with Python. But the strong foundation in algorithms and data structures has helped me grasp different programming languages and concepts with relative ease.


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